So what exactly are force fields good for?

Sue Storm tries hard to use her favorite force field to counter the 1 kcal/mol barrier



Every once in a while there is a study asking what method X (X = docking, free energy calculations, molecular dynamics, force fields etc.) is good for. Such studies can be useful to take stock of a particular paradigm. So the question that Jonathan Goodman and his group ask in this paper is "Are force fields good for reproducing non-bonded interactions, especially hydrogen bonding, pi-stacking and dispersion?". He and his group compare very high-level quantum chemical ab initio data with data obtained from the most commonly used force fields, namely MM2*, MM3*, MMFFs, OPLS-2005 etc. The ab initio data used is from Pavel Hobza who has almost consummately published on these methods. The question is; how well do the force fields do compared to the gold standard? The answer is necessarily incomplete and complex and again raises many interesting questions about the enigmatic role of hydrogen bonding in chemical and biological systems.

The complexes studied include purely pi-stacked complexes, purely hydrogen bonded complexes and mixed complexes where both interactions play roles. Typical examples include alcohol-amide complexes, water oligomers and of course, the classic stacked and hydrogen bonded DNA nucleoside bases. The parameters that the authors looked at were geometries and energies, both of optimized complexes as well as crystal structures.

The results are perhaps not too surprising; the more recent OPLS-2005 and MMFFs are probably the best in reproducing known geometries and energies while MM2* and MM3* don't perform that well in general. As noted in some other studies, at least some of the results for MMFFs and OPLS compare with those obtained with high-level ab initio calculations, thus indicating the value of these cost-effective methods for geometry optimization and energy determination (let's ignore for a moment that solvation models in ab initio methods make even these less than perfect).

What is more important though is that all the force fields are generally not good for reproducing hydrogen bonded systems compared to systems where dispersion, stacking etc. are the key players. This is partly an indication of the tricky events including long-range solvation which play an important role in h-bond formation. But what is interesting is that the methods underestimate the energetics of hydrogen bonds. While I am a little puzzled by this, one of the explanations that comes to my mind regarding this curious fact is that in real systems, h-bonding is a cooperative interaction. An h-bond can pay for loss of entropy, thus making the overall free energy of the next h-bond more favourable. Of course force fields don't calculate free energy, but to a first approximation we can probably assume that the enthalpy and free energy are similar for these simple systems. To be honest, because of the complex nature of long-range dispersion interactions I would have assumed that the force fields would be worse in modeling these. I frankly don't understand why they work better for such interactions but it's an interesting observation.

But now for some general thoughts; it's always worth remembering that for molecules like proteins which are stabilized by h-bonds, the h-bonds when formed are simply swapped for similar bonds with water, thus making a relatively insubstantial contribution to protein stability. It is the large number of such interactions that can tip the balance for a protein, but the real driving force is now universally recognized as the hydrophobic effect and the burial of non-polar groups. Calculations such as those above indicate that because of the fine-tuning of h-bonds that proteins often use to achieve stability, force fields have some way to go in predicting tiny energy differences. It is still a great challenge to model the sub-angstrom geometry optimization of h-bonds that biopolymers achieve. But force fields are hardly unique in not being able to do this; so are other methods which are still trying to break the 1 kcal/mol barrier. Ironically in this study, the mean unsigned error when the hydrogen-bonded complexes are included is about 1 kcal/mol.

So are force fields good for anything at all? The short answer is yes, exemplified by the massive number of publications that regularly use force fields as well as the substantial number of people in academia and industry studying them. Obviously people think they are important, otherwise so many common programs doing everything from protein folding to drug-protein interactions would not have relied on them. I have had reasonable experience with force fields and have always kept in mind a couple of things about them that are worth reiterating:

1. Force fields are usually good at reproducing geometries, and best for reproducing sterics.
2. Force fields are usually not so good at reproducing energies since energy estimation is a function of the special parameterization and convergence criteria unique to every force field (As the Zen master says, "What the answer is depends on what question you ask"). However, relative conformational energies using a single force field for instance may be useful.
3. As a corollary, force fields can be pretty poor for dealing with molecules having a large number of polar functional groups. While this means that peptides are hard to model, modeling of peptides has also been mitigated by the fact that unlike small molecules, the chemistry to be parameterized is limited.
3. Many times the real problem is not with force fields per se but with the accompanying implicit solvation models. Admirable effort has been expended in developing these models but to be honest we still don't understand enough about that enigmatic solvent named water to do a satisfactory job. We are just scratching the surface when it comes to modeling things like solvent entropy for instance.

If you are following the field's developments, you also see an engaging and ongoing debate that pits the "science first" camp against the "parameterization first" camp. The science first camp disapproves of the other camp's efforts to improve their force fields simply by adding more parameters and optimizing against experiment; to them it is much more important to meticulously improve the methodology by incorporating as much real science as possible. The parameterization first camp argues that statistical methods have their honored place in the annals of science and that getting results fast and efficiently is important for application-oriented scientists like drug discovery people. I believe that as in other matters, both sides are right. It is an uncomfortable feeling when you don't truly understand the science behind a method and yet the method works, but at the same time it is important to have a well-parameterized and tested model that could help you in a practical sense, even if incompletely understood.

As with everything else, finally it is an astute application of force fields that takes into account their strengths and limitations which will lead to productive results. One of the most interesting things about doing science involves weighing the pros and cons of methods, techniques and algorithms and deciding what judicious combination would provide the best answer and why. It may not always work, but it could keep us from getting seduced by the dark side of the force (field)

Paton, R., & Goodman, J. (2009). Hydrogen Bonding and π-Stacking: How Reliable are Force Fields? A Critical Evaluation of Force Field Descriptions of Nonbonded Interactions Journal of Chemical Information and Modeling, 49 (4), 944-955 DOI: 10.1021/ci900009f

The anti-question, or when bias can be a good thing

A recent publication indicates that more bias in the form of natural product scaffolds not yet synthesized could improve hit rates in screening



Most drug discovery projects are inaugurated with some kind of screening campaign where millions of molecules are screened against a biological target. Even though the hit rate from High-Throughout Screening (HTS) can be quite low, HTS still provides one of the best starting points to discover interesting new structures that display biological activity. In spite of this, there is frequent disappointment at the low rates from HTS which could be as low as 0.05%.

But instead of focusing on the low hit rate from HTS, what if we express surprise that this hit rate is actually high? This thought takes me into a slight digression. In his remarkable book The Black Swan, the author Nassim Nicholas Taleb talks about an "anti-library", the set of all books you have not read. The anti-library is in some ways more important than your library because it really tells you what you are ignorant about.

Similarly we can define an "anti-question". The anti-question is a question opposite to one which we might usually ask. So instead of asking; "Why is this drug specific for this protein?", we could ask "Why is this drug not hitting other proteins?". The value of the anti-question is that it forces us to analyze and evaluate things that we otherwise may not and enables us to think outside the box. As the wise doctor constantly exhorts detective Sponer in "I Robot" to get to the all-important right question, so it could be important to get to the right anti-question.

In the context of HTS, the anti-question actually turns out to be logical. Instead of asking, "Why is the hit rate from HTS so low"?, one should ask "Given the number of small molecules in small-molecule space (~10*60) compared to the extremely low number typically screened in HTS campaigns (10*6), why should we get any hits from HTS at all?". Even narrowing down the unimaginably large small-molecule universe to more drug-like or lead-like entities, we still run into a numbers paradox since even this number is orders of magnitude greater than what is usually screened.

In their most recent paper, Brian Shoichet and his team ask this important anti-question, and it leads them down an interesting road. Most campaigns that screen libraries focus on readily available commercial compounds and fragments that can be synthesized by organic chemists. This bias in turn reflects what has been more or less synthetically accessible through more than a hundred years of synthesis. Compared to this, the Kyoto Encyclopedia of Genes and Genomes (KEGG) contains metabolites whose structures are untainted by the minds of organic chemists. These are scaffolds among secondary metabolites and natural products that have simply been found.

There is another set of structures; the Generated Database (GDB), a theoretical set which contains all possible molecules containing less than 11 heavy atoms consisting of first-row elements (C, O, N, F). This number is not as large as may be imagined and amounts to about 26 million. In the study the authors essentially compare the set of purchasable or commercial KEBB compounds found in their own annotated library called ZINC with the GDB. They use a similarity measure called a Tanimoto coefficient derived from 2D fingerprint comparison to accomplish this. 2D fingerprints use different kinds of protocols for breaking up a molecule into bit strings and then compare bit strings by distances and atom types.

The comparison indicates something interesting; the compounds in the purchasable set are much more similar to the KEBB compounds than are the compounds from the rest of the GDB. In other words, purchasable compounds contain scaffolds that are biased towards those in the KEBB. This is a good thing, since metabolites are usually primed by nature to show at least some biological activity. Another noteworthy finding was that the bias also increased with molecular size, as compounds became more drug-like or lead-like in terms of size.

However, the more surprising and useful observation was that there are hundreds of scaffolds in the KEBB that are notpresent in the commercial library. The authors also do this comparison for other popular commercial libraries designed specifically for screening and find a similar result. The bottom line; while synthesized commercial libraries of molecules show a bias toward natural products and metabolites, there are also several natural product scaffolds that are not found in these libraries.

So what is the prescription? Introduce further bias! The compounds in the KEGG are more or less optimized for biological activity. If their scaffolds are not yet present in the commercial libraries, organic chemists should go ahead and focus on synthesizing these scaffolds and adding them to screening libraries. More such scaffolds could increase the hit rate in HTS by enriching libraries in biologically relevant scaffolds. Of course the usual caveats of false positives and promiscuous compounds should be kept in mind, and it's also not clear that proteins like kinases which are optimized to bind certain core scaffold structures would greatly benefit from these diverse scaffolds. But in terms of unmined drug space, introducing such further bias would be beneficial.

This study again goes to show the possibilities for finding new stars in the constellations and galaxies of the drug universe. Hopefully the universe will keep on expanding.

Hert, J., Irwin, J., Laggner, C., Keiser, M., & Shoichet, B. (2009). Quantifying biogenic bias in screening libraries Nature Chemical Biology DOI: 10.1038/nchembio.180

Overmedicated overachievers

Since we were on the subject of messing with brain chemistry in the last post, it's worth pointing out an interesting and a more than a little disturbing and provocative article by Margaret Talbot in the New Yorker that deals with the controversial use of stimulant drugs like Adderall and Ritalin.

Talbot especially focuses on Ivy League students who seem to be on a veritable diet of cocktails of these drugs. They not only turn them into supermen and women when it comes to writing papers and doing assignments, but "enhance" their social, romantic and personal lives. Websites on which anonymous users share their experiences with these compounds abound, and these users often are not bashful about sharing not just experiences but samples of such drugs. Nor do doctors seem to hesitate in rather liberally prescribing these medications. Talbot chronicles the experiences of several users who report on an overall enhanced sense of perception and understanding. The phenomenon is of course not limited to Ivy League students, but professors at top schools as well as their students seem to be ideal test cases, considering the pressures of academic life and the myriad ways to cope that they come up with.

So the question naturally is; is this a good thing? The bigger question I want to ask is; in twenty years, when I meet a person, do I want the sum total of his or her personality to be essentially defined by 5 magic pills that are popped into the mouth every morning, like recharging a battery? Are we going to enhance our lives with drugs so much that our intrinsic persona is only vaguely visible, if at all, under a thick blanket of smiles, appropriate social manners, and exuberant behavior that is artificially induced by medication? And of course, do we understand enough about brain chemistry to use these neuroenhancers on a regular basis? (This one's easy; the answer is a terse 'no')

Proponents of the drugs say that these molecules tickle similar receptors in the brain as caffeine. If copious quantities of stimulant black coffee are still kosher, what's wrong with minute quantities of Ritalin taken essentially for the same purpose? It's hard to make an argument against this, but from a long-term perspective I would be much more skeptical about the effects of...I don't know, amphetamines on the brain compared to coffee?. The long-term effects of both Ritalin and Adderall are not known. A related matter is that the temporary stimulation and enhancement induced by these drugs may mask the loss of deeper and important functions that may not be apparent in the short-term. Indeed, perhaps the most troubling side-effect that Talbot documents is a loss of truly creative thinking. As she says, this is not surprising. Truly creative thinking often happens when the mind is wandering, when one is not too focused on a particular task. Ritalin-like compounds that may bring about intense spells of concentration may deprive us of those strokes of insights that actually result from a scatter-brained loss of focus.

Another practical issue that these medicines pose is that of unduly ramping up competitiveness. Consider that your co-worker is on these medicines and it's apparently enhancing his or her productivity. Would you feel pressured to aid your normal faculties with a boost of these babies? Wouldn't you like to stay competitive by asking your doctor for Ritalin so that you are sure that it's you and not your co-worker who bags that lucrative contract or job position? In an era where competitiveness has becoming so mind-numbing that's it's hardly noticed, do we need more incentives for competing even harder? It's a question that is going to constantly rear its head.

In the end though, I have a problem with these enhancers for the same reason that I have a problem with antidepressants. We are in an era where ordinary problems like shyness are being presented as "disorders" that may benefit from a pill. Attention Deficit Hyperactive Disorder is of course a real, clinical manifestation. But aren't all of us attention deprived to varying extents during the day. Simply as a scientific fact, wouldn't it generally help us if all of us take Ritalin? Who wants to be a member of Ritalin nation?

But that's just my opinion. Our parents' and grandparents' generations exemplified the maxim "Where there is a will, there's a way". Maybe for us it's going to be, "Where there's no will, there is a pill". I cannot wait for the singularity.

Atypically typical: The single vs multiple compound hypothesis in schizophrenia

One of the most painful parts in the book "A Beautiful Mind" narrates how the brilliant mathematician John Nash was admitted to a Trenton hospital and subjected to what was then one of the most fashionable treatments for schizophrenia- insulin shock therapy. The periodic administrations of large doses of insulin to induce convulsions and coma not only was embarrassing for the future Nobel Laureate and his family but it may have possibly damaged parts of his mind- and not just brain- beyond repair. It may have scarred a beautiful mind.

But we had lobotomy, and we had insulin shock therapy. And then we evolved. The drugs chlorpromazine and reserpine revolutionized the treatment of schizophrenia in the 1950s (recall the movie "Awakenings"). Since then a variety of drugs have been used for mitigating the symptoms of this devastating disorder. However most of these drugs target what are called the "positive symptoms" of the disease, which include delusions, agitation and hallucinations. The "negative symptoms" include social withdrawal, depression and poverty of speech, symptoms not targeted by many drugs. More importantly, many of the early drugs had nasty side effects, termed "extrapyramidal symptoms" (EPS) which included involuntary twitching of facial and other muscles, part of what is termed tardive dyskinesia. A lot of focus has been put over the years on reducing these effects as well as in mitigating negative symptoms. Medicines supposed to achieve these goals have been traditionally termed "atypical antipsychotics"

Now an article co-authored by Nobel Laureate Arvid Carlsson questions this widely accepted definition of atypical antipsychotics and suggests that the definition actually hampered the development of these drugs for more than 30 years. The article contains some rather technical commentary, but what I could get from it is the following: the most widely accepted hypothesis for the etiology of schizophrenia is the so-called "dopamine hypothesis", pioneered by Carlsson himself, that contends that high levels of dopamine in the brain are associated with psychoses. Drugs like clozapine are supposed to prevent dopamine metabolism by binding especially to the D2 family of dopamine receptors. These drugs bind to other receptors too but it's their action at dopamine D2 receptors that's important in managing the symptoms of schizophrenia.

Carlsson contends that the flaw in 30 years of antipsychotic therapy lies in searching for the perfect "atypical" antipsychotic which will tackle both positive and negative symptoms of schizophrenia as well as EPS. It was believed for many years that all these effects could not be disentangled from each other and necessarily went together. This led to the search for a "magic bullet", a single compound that could hit all symptoms. Carlsson says that recent studies on the action of antipsychotics suggests different mechanisms responsible for different symptoms, including mechanisms involving novel receptors that were not implicated before. The drugs also cause different levels of occupancy for D2 receptors in different tissues and parts of the brain, and thus provide the opportunity for designing multiple compounds that hit subtypes in different places. According to Carlsson, the "atypical" compounds used to treat psychoses should actually be called "typical" since they usually do a good job of treating the positive symptoms of the disease. The bottom line is that multiple avenues for treating the symptoms of schizophrenia arising from different molecular mechanisms should be explored, instead of focusing on a single compound that would encompass all features. Different compounds should be used for targeting positive and negative symptoms.

To me this narrative reinforced what is becoming clear about CNS disorders and the accompanying therapy; that non-selective drugs targeting different mechanisms are often more beneficial than single, selective drugs targeting only one receptor, and that multiple pathways affect the development of a disease whose symptoms and side-effects may be classified into distinct categories only with deceptive convenience. The brain is the most complex structure known to man. Its manipulation and the treatment of its disorders deserves an approach that is not too less complex and nuanced.

Gründer, G., Hippius, H., & Carlsson, A. (2009). The 'atypicality' of antipsychotics: a concept re-examined and re-defined Nature Reviews Drug Discovery, 8 (3), 197-202 DOI: 10.1038/nrd2806

Presence of absence is not absence of presence

The proximate cause of my absence from this blog has been the tribulations of settling down in the most lawless state in the country. Just kidding, but two things about the Garden State are axiomatic truths:

A: The shortest route from A to B is most generally not the shortest route from B to A. Heisenberg would have been pleased. Traffic circles, one-way streets, deer roadkill on Route 202 gradually disintegrating for three days, drivers who must be thinking they are competing in Formula-1 and potholes on roads that seem like they are designed to retain 1920s charm all make the picture endearingly complete.

B: If you are in Princeton you should expect to see photos of Einstein eating ice cream, Oppenheimer licking his fingers after eating buffalo wings at Chuck's and John von Neumann balancing a paper cone filled with popcorn on his generous belly.

Ok, I made the last two up, but I did see the first one; Einstein somewhat disinterestedly licking an ice cream cone in one of those small, family-owned ice cream stores on Nassau Street whose name I will have to look up again. Actually this fact about Einstein should not surprise one at all: the man took as much pleasure in ice cream and all the simple joys of life as in tensor calculus. In the 1950s, according to his own admission, Princeton was a "quaint ceremonial village, occupied by demigods on stilts". The quaintness still somewhat lingers but the stilts have definitely given way to big cars that block traffic and pedestrian access. As for the rest of the state, what was George Merck thinking?

I hope to explore more of the village on the weekend, after I have finally moved into an apartment. I also hope to get a bite of the Apple and of some docking and chlorine-pi interactions. Work and the postdoc has started and all I can say is that it involves trying to model what are currently seeming to be unmodelable (?) proteins.

For the love of science

So I am trying to find possible groups interested in science meeting up in NJ and my friend suggests this site called meetup.com. So I type in my zip code and ask the site to find people or groups interested in "science" within 25 miles. Among the twenty or so hits are included "The NY/NJ group for parents with science careers" (11 members) and "Red Bank Life Science Discussion Group" (4 members). Good for them, but almost everything else includes things like

The Monroe Township Law of Attraction Meetup Group (28 members)
Princeton NJ ~Tantra Awakening ~The Art Of Conscious Loving (42 members)
Princeton Holistic Clinic (98 members)
The Central New Jersey Astrology Meetup Group (6 members)
The Healers' Guild (37 'adepts')

Both the predominance of these groups and their member counts indicate that the 5+ years that I spent learning and doing science in graduate school were futile after all. But at least it does seem that we are catching up with astrology.

State of the Garden

I am finally in the Garden State after a rather protracted road trip amply filled with fever and a cold brought on by all the allergies dust stirred up during packing (I think there's a Nobel Prize in the wings for discovering the true nature of allergies).

The first thing I rather surprisingly notice is how much more tiring it is to drive here compared to Atlanta where the drivers are supposed to be rather rash. Plus, the constant exits and bifurcations on several different highways, routes and streets that one needs to traverse to travel quite non-linearly between any two points is frustrating. I am also hoping to actually see some gardens in the garden state. My hundreds of books also made the journey here although I have to still check how many of them are still in pristine condition. And I am still looking for an apartment while I am comfortably imposing myself on my cousin at his place.

For now I am aiming to hang out a little at the bookstores and cafes in nearby Princeton, hoping to meet some like-minded people. If you want to pointlessly muse and pontificate and don't feel scared in meeting strange new people, drop me a line. I would also appreciate it if anyone could bring their wisdom to bear on three things important to me; good bookstores (other than the ubiquitous Borders), good movie theaters where one can especially catch off-beat or foreign movies, and good restaurants and cafes where one can stare blankly at nothingness for hours and read.

The 2009 Lindau Nobel Laureates Meeting

It is a great privilege for me to be invited to live-blog and write about the 2009 Lindau Nobel Prize Winners meeting in the scenic Bavarian town of Lindau, Germany. Since 1951, dozens of Nobel laureates have been joined every year by about 500 carefully chosen students from around the world for a full week of informal discussions, seminars, lunches and lectures where students and Nobelists mingle with each other and one can find at least one laureate on every square foot of the floor no matter what direction he looks.

This year's focus is on chemistry and an august list of no less than 22 Nobel Prize winners in the subject is going to gather in this scenic town. I am honored to be invited because of my background in chemistry and blogging and relish the opportunity like nothing else. I am supposed to be on a small team of 7 journalists and bloggers blogging the event for scienceblogs.com and scienceblogs.de. Along with Matthew Chalmers who is an editor and writer for several publications like New Scientist and the Times, I will largely be responsible for writing about the event in English for Scienceblogs.com. The writing will include both general observations about the meeting as well as descriptions of the talks and seminars. Hopefully I can bring it all together.

Nobel laureates have long been a particular interest of mine. People interested in this kind of a thing collect Nobel statistics like sports and stock market statistics; it was only when exploring facts about youngest, oldest, tallest, most awarded, famous father-son duos, and most neglected non-winners that I realised the allure of cricket or sensex figures.

Calling the list of scheduled speakers at Lindau stellar is a futile and redundant effort because every one of them has won the highest honor in his or her field. Many of the names are familiar and not only have I long admired these people, but I have even directly and indirectly used their work in my own research, as have thousands of scientists and students around the world. Now we will all experience a connection to our work like no other.

In any case, this is as magnificent a concatenation of minds as you can expect to find and I am immensely looking forward to it. The meeting is going to be held from June 28 - July 3. 22 Nobelists in one of the most beautiful parts of the world. It does not get better than this. I will naturally keep on updating.

And you wonder why atheists bristle when the religious call them 'intolerant'

A very reasonable religious woman writes in on a show to ask Pat Robertson how she could strike a middle ground between herself and her boyfriend who is an atheist. He has stuck by her for a long time and the two obviously are quite close. Do we need to guess how the Reverend Robertson responds? Remember that this guy is still worshipped by millions of people. And they say Richard Dawkins is 'intolerant' of religious people.

Drug Discovery, Models and Computers: A (necessarily incomplete) Personal Take

Drugs and rational drug discovery

Natural substances have been used to treat mankind’s diseases and ills since the dawn of humanity. The Middle Ages saw the use of exotic substances like sulfur and mercury to attempt to cure afflictions; most of these efforts resulted in detrimental side effects or death because of lack of knowledge of drug action. Quinine was isolated from the bark of the Cinchona tree and used for centuries to treat malaria. Salicylic acid was isolated from the Willow tree and was used for hundreds of years to treat fevers, knowledge that led to the discovery of Aspirin. The history of medicine has seen the use of substances ranging from arsenic to morphine, some of which are now known to be highly toxic or addictive.

The use of these substances reflected the state of medical knowledge of the times, when accidentally generated empirical data was the most valuable asset in the treatment of disease. Ancient physicians from Galen to Sushruta made major advances in our understanding of the human body and of medical therapies, but almost all of their knowledge was derived through patient and meticulously documented trial and error. A lack of knowledge of the scientific basis of disease meant that there were few systematic rational means of discovering new medicines, and serendipity and the traditional folk wisdom passed on through the centuries played the most important role in warding off disease.

This state of affairs continued till the 19th and 20th centuries when twin revolutions in biology and chemistry made it possible to discover drugs in a more logical manner. Organic chemistry formally began in 1848 when Friedrich Wöhler found that he could synthesize urea from simple inorganic substances like ammonium cyanate, thus dispelling the belief that organic substances could only be synthesized by living organisms (1). The further development of organic chemistry was orchestrated by the formulation of the structural theory in the late 19th century by Kekulé, Cooper, Kolbe, Perkin and others (1). This framework made it possible to start to elucidate the precise arrangement of atoms in biologically active compounds. Knowledge of this arrangement in turn led to routes for synthesis of these molecules. These investigations also provided impetus to the synthesis of non-natural molecules of practical interest, sparking off the field of synthetic organic chemistry. However, while the power of synthetic organic chemistry later provided several novel drugs, the legacy of natural products is still prominent, and about half of the drugs currently on the market are either natural products or derived from natural products (2).

Success in the application of chemistry to medicine was exemplified in the early 20th century by tentative investigations of what we currently call structure-activity relationships (SAR). Salvarsan, an arsenic compound used for treating syphilis, was perhaps the first example of a biologically active substance that had been improved by systematic investigation and modification. As the same time, chemists like Emil Fischer were instrumental in synthesizing further naturally occurring substances like carbohydrates and proteins, thus extending the scope of organic synthesis into biochemistry.

The revolution in structure determination initiated by physicists led to vastly improved synthesis and studies of bioactive substances. At this point, rational drug discovery began to take shape. Chemists working in tandem with biologists made hundreds of substances which were tested for their efficacy against various diseases. Knowledge from biological testing was in turn translated into modifications of the starting compounds. The first successful example of such rational efforts was the synthesis of sulfa drugs used to treat infections in the 1930s (3). These compounds were the first effective antibiotics and were followed by the famous discovery, but this time serendipitous, of penicillin by Alexander Fleming in 1928 (4).

Rational drug discovery received a substantial impetus because of the post-World War 2 breakthroughs of structure determination by x-ray crystallography that revealed the structures of small molecules, proteins and DNA. The discovery of the structure of DNA in 1953 by Watson and Crick heralded the advent of molecular biology (5). This landmark event led in succession to the elucidation of the genetic code and the transfer of genetic information from DNA to RNA that results in protein synthesis. The first structure determination of a protein- hemoglobin by Perutz (6)- was followed by the structure determination of several other proteins, some of which were pharmacologically important. Such advances and preceding ones by Pauling and others (7) led to the elucidation of common motifs in proteins such as alpha helices and beta sheets. The simultaneous growth of techniques in biological assaying and enzyme kinetics made it possible to monitor the binding of drugs to biomolecules. At the same time, better application of statistics and the standardization of double blind, controlled clinical trials caused a fundamental change in the testing and approval of new medicines. A particularly noteworthy example of one of the first drugs discovered through rational investigations is cimetidine (8), a drug for acid reflux that was for several years the best-selling drug in the world.

Structure-based drug design and CADD

As x-ray structures of protein-ligand complexes began to emerge in the 70s and 80s, rational drug discovery received enormous benefits. The development was also accompanied by High-Throughput Screening, an ability to screen thousands of ligands against a protein target to identify likely binders. These studies led to what today is known as “structure-based drug design” (SBDD) (9). In SBDD, the structure of a protein bound to a ligand is used as a starting point for further modification and improvement of properties of the drug. While care has to taken in order to fit the structure well to the electron density in the data (10), well-resolved data can greatly help in identifying points of contact between the drug and the protein active site as well as the presence of special chemical moieties such as metals and cofactors. Water molecules identified in the active site can play crucial roles in bridging interactions between the protein and ligand (11). Early examples of classes of drugs discovered using structure-based design include Captopril (12) (angiotensin-converting enzyme inhibitor- hypertension) and Trusopt13 (carbonic anhydrase inhibitor- glaucoma) and recent examples include Aliskiren (14) (renin inhibitor- hypertension) and HIV protease inhibitors (13).

As SBDD progressed, another approach called ligand-based design (LBD) has also recently emerged. Obtaining x-ray structures of drugs bound to proteins is still a tricky endeavor, and one is often forced to proceed on the basis of the structure of an active compound alone. Techniques developed to tackle this problem involve QSAR (Quantitative Structure-Activity Relationships) (15) and pharmacophore construction in which the features essential for a particular ligand to bind to a certain protein are conjectured from affinity data for several similar and dissimilar molecules. Molecules based on the minimal set of interacting features are then synthesized and tested. However, since molecules can frequently adopt diverse conformations when binding to a protein, care has to be exercised in developing such hypotheses. In addition, it is relatively easy to be led astray by a high correlation between affinity data in the training set. It is paramount in such cases to remember the general discrepancy between correlation and causation, and overfitting of models can lead to both spurious correlations and absence of causation (16). While LBD is more recent than SBDD, it has turned out to be valuable in certain cases. Noteworthy is a recent example where an inhibitor of NAADP was discovered by shape-based virtual screening (17) (vida infra)

As rational drug discovery progressed, software and hardware capacities of computers also grew exponentially, and CADD (Computer-Aided Drug Design) began to be increasingly applied to drug discovery. An effort was made to integrate CADD in the traditional chemistry and biology workflow and its principal development took place in the pharmaceutical industries, although academic groups were also instrumental in developing some capabilities (18). The declining costs of memory and storage, increasing processing power and facile computer graphics software put CADD within the grasp of relatively untrained computational chemists or experimental scientists. While the general verdict on the contribution of CADD to drug discovery is still forthcoming, many drugs currently on the market now include CADD as an important component of their discovery and development (19). Many calculations that once were impractical because of constraints of time and computing power can now be routinely performed, some on a common desktop. Currently the use of CADD in drug design aims to address three principal problems, all of which are valuable to drug discovery.

Virtual Screening

Virtual screening (VS) is defined by the ability to test thousands or millions of potential ligands against a protein, distinguish the actives from inactives and rank the ‘true’ binders in a certain top fraction. If validated, VS would serve as a valuable complement, if not substitute, for HTS and would save significant amounts of resources and time in HTS. Just like HTS, VS has to circumvent the problem of false positives and false negatives, the latter of which in some ways are more valuable since by definition they would not be identified. VS can be either structure-based or ligand-based. Both approaches have enjoyed partial success although recent studies have validated 3D ligand-based techniques in which ligand structures are compared to known active ligands by means of certain metrics as having a greater hit rate than structure-based techniques (20). Virtual libraries of molecules such as DUD (21) (Directory of Useful Decoys) and ZINC (22) have been built to test the performance of several VS programs and compare them with each other. These libraries typically consist of a few actives and several thousand decoys, with the goal being to rank the true actives above the true decoys using some metric.

Paramount in such retrospective assessment is an accurate method for evaluating the success and failure of these methods (23,24). Until now ‘enrichment factors’ have mostly been used for this purpose (24). The EF refers to the number of ‘true’ actives that rank in a certain top fraction (typically 1% or 10%) as a function of the screened database. However the EF suffers from certain drawbacks, such as being dependent on the number of decoys in the dataset. To circumvent this problem, recent studies have suggested the use of the ROC (Receiver Operator Characteristic) curve, a graph that plots false positives vs. true positives (24,25) (Figure 1). The curve indicates what the false positive rate is for a given true positive rate and the measured variable is the Area Under the Curve (AUC). A completely random performance gives a straight line (AUC 0.5), while better performance results in a hyperbolic curve (AUC > 0.5).




Figure 1: ROC curve for three different VS scenarios. Completely random performance will give the straight white line (AUC 0.5), an ideal performance (no false positives and all true positives) will give the red line (AUC 1.0) and a good VS algorithm will produce the yellow curve (0.5 < AUC < 1.0)

Until now VS has provided limited evidence of success. Yet its capabilities are being improved and it has become a part of the computational chemist’s standard repertoire. In some cases VS can provide more hits compared to HTS (26) and in others, VS at the very least provides a method to narrow down the number of compounds actually assayed (27). As advances in general SBDD and LBD continue, the power of VS to identify true actives will undoubtedly increase.

Pose-prediction

The second goal sought by computational chemists is to predict the binding orientation of a ligand in the binding pocket of a protein, a task that falls within the domain of SBDD. This endeavor if successful will provide an enormous benefit in cases where crystal structures of protein-ligand complexes are not easily obtained. Since such cases are still very common, pose-prediction continues to be both a challenge as well as a valuable objective. There are two principal problems in pose prediction. The first one relates to the scoring of the poses obtained in order to identify the top-scoring pose as the ‘real’ pose; current docking programs are notorious for their scoring unreliability, certainly in an absolute sense and sometimes even in a relative sense. The problem of pose prediction ultimately is defined by the ability of an algorithm to find the global minimum orientation and conformation of a ligand on the potential energy surface (PES) generated by the protein active site (28). As such it is susceptible to the common inadequacies inherent in comprehensively sampling a complex PES. Frequently however, as in the case of CDK7, past empirical data including knowledge of poses of known actives (roscovitine in this case) provides confidence about the pose of the unknown ligand.

Another serious problem in pose prediction is the inability of many current algorithms to adequately sample protein motion. X-ray structures provide only a static snapshot of ligand binding that may obscure considerable conformational changes in protein motifs. Molecular dynamics simulations followed by docking (‘ensemble docking’) have remedied this limitation to some extent (29), induced-fit docking algorithms have now been included in programs such as GLIDE30, and complementary information from dynamical NMR studies may help judicious selection between several protein poses. Yet simulating large-scale protein motions are still outside the domain of most MD simulations, although significant progress has been made in recent years (31,32).

An example of how pose prediction can shed light on anomalous binding modes and possibly save the allocation of time and financial resources was experienced by the present author during his study of a paper detailing the development of inhibitors of the p38 MAP kinase (33). In one instance the authors followed the SAR data in the absence of a crystal structure and observed contradictory changes in activity influenced by structural modifications. Crystallography on the protein ligand complex finally revealed an anomalous conformation of the ligand in which the oxygen of an amide at the 2 position of a thiophene was cis to the thiophene sulfur, when chemical intuition would have expected it to be trans. The crystal structure showed that an unfavorable interaction of a negatively charged glutamate with the sulfur in the more common trans conformation forced the sulfur to adopt the slightly unfavorable cis position with respect to the amide oxygen. Surprisingly this preference was seen in all top 5 GLIDE poses of the docked compound. This example indicates that at least in some cases pose prediction could serve as a valuable timesaving complement and possible alternative to crystallography.

Binding affinity prediction

The third goal is possibly the most challenging endeavor for computational chemistry. Rank-ordering ligands in terms of their binding affinity involves accurate scoring, which as noted above is a recalcitrant problem. The problem is a fundamental one since it really involves calculating absolute free energies of protein ligand binding. The most accurate and sophisticated approaches for calculating these energies are the Free-Energy Perturbation (FEP) (34) or Thermodynamic Integration (TI) methods based on MD simulations and statistical thermodynamics. The methods involve ‘mutating’ one ligand to another in hundreds of thousands of infinitesimal steps and evaluating the binding enthalpy and entropy at every step. As of now, these techniques are some of the most computationally expensive techniques in the field. This problem typically limits their use only to evaluating free energy changes between ligand that differ little in structure. Therefore successful examples where they have found their greatest use involve cases where small substituents on aromatic rings are modified to evaluate changes in binding affinity (35). However as computing power grows, these techniques will continue to find more applications in drug discovery.

Apart from these three goals, a major goal of computational science in drug discovery is to aid the later stages of drug development when pharmacokinetics (PK) and ADMET (Absorption Distribution Metabolism Excretion Toxicity) issues are key. Optimizing the binding affinity of a particular compound to a protein only results in an efficient ligand and not necessarily an efficient drug. Computational chemistry can make valuable contributions to these later developmental stages by trying to predict the relevant properties of ligands in the early stages, thus limiting the typically high attrition of drugs in the advanced phases. While much remains to be accomplished in this context, some progress has been made (36). For example, the well-known Lipinski Rule of Five (37) provides a set of physicochemical properties necessary for drugs to have good bioavailability and computational approaches are starting to help evaluate these properties during early stages. The QikProp program developed by Jorgensen et al. calculates properties like Caco-2 cell permeability, possible metabolites, % absorption in the GI tract and logP values (38). Such programs are still largely empirical, depending on a large dataset of properties of known drugs for comparison and fitting.

Models, computers and drug discovery

In applying models to designing drugs and simulating their interactions with proteins, the most valuable lesson to remember is that these are models that are generated by computers. Models seldom mirror reality; in fact they often may succeed in spite of reality. Models are not usually designed to simulate reality but they are designed to produce results that agree with experiment. There are many approaches that produce such results. These approaches may not always encompass factors operating in real environments. In QSAR for instance, it has been shown that adding enough number of parameters to your model can lead to a good fit to the data with a high correlation coefficient. However the model may be overfitted; that is, it may seem to fit the known data very well but may fail to predict the unknown data, which is what it was designed to do (16,39). In such cases, using more advanced statistical methods and using ‘bootstrapping’ (leaving out a part of the data and looking at the resulting fit to investigate whether that part of data is predicted) can lead to improvement in results (39).

Models can also be used in spite of outliers. A high correlation coefficient of 0.85 that leads to acceptance of a model may nonetheless lead to one or two outliers. It then becomes important to be aware of the physical anomaly which the outliers represent. The reason for this is clear. If the variable producing the outlier does not constitute a part of the model building, then applying the well-trained model to a system where that particular variable suddenly becomes dominant will result in a failure of the model. Such outliers, termed ‘black swans’, can prove extremely deleterious if their value is unusually high (40). This phenomenon is known to operate in the field of financial engineering (40). In modeling for instance, if the training set for a docking model consists of largely lipophilic protein active sites, then the model may fail to deliver cogent results if applied to a set of ligands binding to a protein that has an anomalously polar or charged active site. If the value of this protein is unusually high for a particular pharmaceutical project, an inability to predict its behavior under unforeseen circumstances may lead to valuable losses. Clearly in this case the physical variable, namely the polarity of the active site, was not taken into account in spite of the fact that the model delivered a high initial correlation merely because of the addition of a large number of parameters or descriptors, none of which was related in a significant way to the polarity of the binding pocket. The difference between correlation and causation is especially relevant in this respect. This hypothetical example illustrates one of the limitations of models iterated above; that they may not bear relationship to actual physical phenomena and may yet fit the data well enough because of various reasons to elicit confidence in their predictive ability.

In summary, models of the kind that are used in computational chemistry have to be carefully evaluated, especially in the context of practical applications like drug discovery where time and financial resources are valuable. Training the model on high-quality datasets, reiterating the difference between correlation and causation and better application of statistics and bootstrapping can help to avert model failure.

In the end however, it is experiment that is of paramount importance for building the model. Inaccurate experimental data with uncertain error margins will undoubtedly hinder the success of every subsequent step in model building. To this end, generating, presenting and evaluating accurate experimental data is a responsibility that needs to be fulfilled by both computational chemists and experimentalists, and it is only a fruitful and synergistic alliance between the two groups that can help overcome the complex challenges in drug discovery.


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